




'''simple docstring'''


import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_					=   {
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
UpperCAmelCase_					=   {
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
UpperCAmelCase_					=   {
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase_					=   {
    "facebook/dpr-ctx_encoder-single-nq-base": 5_12,
    "facebook/dpr-ctx_encoder-multiset-base": 5_12,
}
UpperCAmelCase_					=   {
    "facebook/dpr-question_encoder-single-nq-base": 5_12,
    "facebook/dpr-question_encoder-multiset-base": 5_12,
}
UpperCAmelCase_					=   {
    "facebook/dpr-reader-single-nq-base": 5_12,
    "facebook/dpr-reader-multiset-base": 5_12,
}


UpperCAmelCase_					=   {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
UpperCAmelCase_					=   {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
UpperCAmelCase_					=   {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}





class  __lowercase    (	__magic_name__			):
   _a    						=   VOCAB_FILES_NAMES
   _a    						=   CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   _a    						=   CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a    						=   CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
   _a    						=   DPRContextEncoderTokenizer





class  __lowercase    (	__magic_name__			):
   _a    						=   VOCAB_FILES_NAMES
   _a    						=   QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   _a    						=   QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a    						=   QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
   _a    						=   DPRQuestionEncoderTokenizer

UpperCAmelCase_					=   collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

UpperCAmelCase_					=   collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


UpperCAmelCase_					=   r"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "





@add_start_docstrings(__magic_name__			)
class  __lowercase    :







   def __call__(						self   ,				UpperCamelCase   ,				UpperCamelCase = None   ,				UpperCamelCase = None   ,				UpperCamelCase = False   ,				UpperCamelCase = False   ,				UpperCamelCase = None   ,				UpperCamelCase = None   ,				UpperCamelCase = None   ,				**UpperCamelCase   ,				)					->     BatchEncoding:
     if titles is None and texts is None:
       return super().__call__(
           UpperCamelCase   ,				padding=UpperCamelCase   ,				truncation=UpperCamelCase   ,				max_length=UpperCamelCase   ,				return_tensors=UpperCamelCase   ,				return_attention_mask=UpperCamelCase   ,				**UpperCamelCase   ,				)
     elif titles is None or texts is None:
       __a								=       titles if texts is None else texts
       return super().__call__(
           UpperCamelCase   ,				UpperCamelCase   ,				padding=UpperCamelCase   ,				truncation=UpperCamelCase   ,				max_length=UpperCamelCase   ,				return_tensors=UpperCamelCase   ,				return_attention_mask=UpperCamelCase   ,				**UpperCamelCase   ,				)
     __a								=       titles if not isinstance(UpperCamelCase   ,				UpperCamelCase   ) else [titles]
     __a								=       texts if not isinstance(UpperCamelCase   ,				UpperCamelCase   ) else [texts]
     __a								=       len(UpperCamelCase   )
     __a								=       questions if not isinstance(UpperCamelCase   ,				UpperCamelCase   ) else [questions] * n_passages
     assert len(UpperCamelCase   ) == len(
         UpperCamelCase   ), f"There should be as many titles than texts but got {len(UpperCamelCase   )} titles and {len(UpperCamelCase   )} texts."
     __a								=       super().__call__(UpperCamelCase   ,				UpperCamelCase   ,				padding=UpperCamelCase   ,				truncation=UpperCamelCase   )['input_ids']
     __a								=       super().__call__(UpperCamelCase   ,				add_special_tokens=UpperCamelCase   ,				padding=UpperCamelCase   ,				truncation=UpperCamelCase   )['input_ids']
     __a								=       {
         'input_ids': [
             (encoded_question_and_title + encoded_text)[:max_length]
             if max_length is not None and truncation
             else encoded_question_and_title + encoded_text
             for encoded_question_and_title, encoded_text in zip(UpperCamelCase   ,				UpperCamelCase   )
         ]
     }
     if return_attention_mask is not False:
       __a								=       []
       for input_ids in encoded_inputs["input_ids"]:
         attention_mask.append([int(input_id != self.pad_token_id   ) for input_id in input_ids]   )
       __a								=       attention_mask
     return self.pad(UpperCamelCase   ,				padding=UpperCamelCase   ,				max_length=UpperCamelCase   ,				return_tensors=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase = 16   ,				UpperCamelCase = 64   ,				UpperCamelCase = 4   ,				)					->     List[DPRSpanPrediction]:
     __a								=       reader_input['input_ids']
     __a						,							__a						,							__a								=       reader_output[:3]
     __a								=       len(UpperCamelCase   )
     __a								=       sorted(range(UpperCamelCase   )   ,				reverse=UpperCamelCase   ,				key=relevance_logits.__getitem__   )
     __a								=       []
     for doc_id in sorted_docs:
       __a								=       list(input_ids[doc_id]   )
       # assuming question & title information is at the beginning of the sequence
       __a								=       sequence_ids.index(self.sep_token_id   ,				2   ) + 1  # second sep id
       if sequence_ids[-1] == self.pad_token_id:
         __a								=       sequence_ids.index(self.pad_token_id   )
       else:
         __a								=       len(UpperCamelCase   )

       __a								=       self._get_best_spans(
           start_logits=start_logits[doc_id][passage_offset:sequence_len]   ,				end_logits=end_logits[doc_id][passage_offset:sequence_len]   ,				max_answer_length=UpperCamelCase   ,				top_spans=UpperCamelCase   ,				)
       for start_index, end_index in best_spans:
         start_index += passage_offset
         end_index += passage_offset
         nbest_spans_predictions.append(
             DPRSpanPrediction(
                 span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]   ,				relevance_score=relevance_logits[doc_id]   ,				doc_id=UpperCamelCase   ,				start_index=UpperCamelCase   ,				end_index=UpperCamelCase   ,				text=self.decode(sequence_ids[start_index : end_index + 1]   )   ,				)   )
       if len(UpperCamelCase   ) >= num_spans:
         break
     return nbest_spans_predictions[:num_spans]







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				)					->     List[DPRSpanPrediction]:
     __a								=       []
     for start_index, start_score in enumerate(UpperCamelCase   ):
       for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]   ):
         scores.append(((start_index, start_index + answer_length), start_score + end_score)   )
     __a								=       sorted(UpperCamelCase   ,				key=lambda UpperCamelCase   : x[1]   ,				reverse=UpperCamelCase   )
     __a								=       []
     for (start_index, end_index), score in scores:
       assert start_index <= end_index, f"Wrong span indices: [{start_index}:{end_index}]"
       __a								=       end_index - start_index + 1
       assert length <= max_answer_length, f"Span is too long: {length} > {max_answer_length}"
       if any(
           start_index <= prev_start_index <= prev_end_index <= end_index
           or prev_start_index <= start_index <= end_index <= prev_end_index
           for (prev_start_index, prev_end_index) in chosen_span_intervals   ):
         continue
       chosen_span_intervals.append((start_index, end_index)   )

       if len(UpperCamelCase   ) == top_spans:
         break
     return chosen_span_intervals





@add_end_docstrings(__magic_name__			)
class  __lowercase    (	__magic_name__							,				__magic_name__			):
   _a    						=   VOCAB_FILES_NAMES
   _a    						=   READER_PRETRAINED_VOCAB_FILES_MAP
   _a    						=   READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a    						=   READER_PRETRAINED_INIT_CONFIGURATION
   _a    						=   ["""input_ids""", """attention_mask"""]
   _a    						=   DPRReaderTokenizer









'''simple docstring'''


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, TensorType

UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}





class  __lowercase    (	__magic_name__			):
   _a    						=   """imagegpt"""
   _a    						=   ["""past_key_values"""]
   _a    						=   {
       """hidden_size""": """n_embd""",
       """max_position_embeddings""": """n_positions""",
       """num_attention_heads""": """n_head""",
       """num_hidden_layers""": """n_layer""",
   }







   def __init__(						self   ,				UpperCamelCase=512 + 1   ,				UpperCamelCase=32 * 32   ,				UpperCamelCase=512   ,				UpperCamelCase=24   ,				UpperCamelCase=8   ,				UpperCamelCase=None   ,				UpperCamelCase="quick_gelu"   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=1e-5   ,				UpperCamelCase=0.02   ,				UpperCamelCase=True   ,				UpperCamelCase=True   ,				UpperCamelCase=False   ,				UpperCamelCase=False   ,				UpperCamelCase=False   ,				**UpperCamelCase   ,				)					->     int:
     __a								=       vocab_size
     __a								=       n_positions
     __a								=       n_embd
     __a								=       n_layer
     __a								=       n_head
     __a								=       n_inner
     __a								=       activation_function
     __a								=       resid_pdrop
     __a								=       embd_pdrop
     __a								=       attn_pdrop
     __a								=       layer_norm_epsilon
     __a								=       initializer_range
     __a								=       scale_attn_weights
     __a								=       use_cache
     __a								=       scale_attn_by_inverse_layer_idx
     __a								=       reorder_and_upcast_attn
     __a								=       tie_word_embeddings

     super().__init__(tie_word_embeddings=UpperCamelCase   ,				**UpperCamelCase   )





class  __lowercase    (	__magic_name__			):







   @property
   def 		UpperCamelCase__      (						self   )					->     Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('input_ids', {0: 'batch', 1: 'sequence'}),
         ]   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = 1   ,				UpperCamelCase = -1   ,				UpperCamelCase = False   ,				UpperCamelCase = None   ,				UpperCamelCase = 3   ,				UpperCamelCase = 32   ,				UpperCamelCase = 32   ,				)					->     Mapping[str, Any]:
     __a								=       self._generate_dummy_images(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )
     __a								=       dict(preprocessor(images=UpperCamelCase   ,				return_tensors=UpperCamelCase   )   )

     return inputs



